Jupiter Text To Video

Skip the learning curve of professional editing software. Describe what you want — turn this text description into a 30-second cinematic video clip — and get...

Security Scan

The skill's requirements and runtime instructions largely match its stated purpose (text→video via a remote API); nothing appears to be asking for unrelated credentials or installs, though the skill will access the network and some local metadata (skill frontmatter/install path).

Purpose & Capability

The skill claims to convert text into videos and only requires a single API token (NEMO_TOKEN) and remote API calls to mega-api-prod.nemovideo.ai, which is coherent with the described functionality. Minor inconsistency: the SKILL.md frontmatter declares a config path (~/.config/nemovideo/) and platform-detection based on install path, but the registry metadata listed no required config paths—this is a small mismatch between metadata and the instructions.

Instruction Scope

Instructions stay within the expected scope (establish session, send messages, upload files, poll render status). They do instruct the agent to: (a) read the skill's frontmatter at runtime to populate attribution headers, and (b) detect the install platform by checking common install paths (~/.clawhub/ or ~/.cursor/skills/). Those filesystem reads are limited and explainable for attribution, but they do require local file/path access that wasn't fully reflected in registry metadata.

Install Mechanism

No install spec or code files are present (instruction-only). That is the lowest-risk model: nothing is downloaded or written by the skill itself.

Credentials

The only required credential is NEMO_TOKEN (primaryEnv). The skill documents using an anonymous-token endpoint if no token is present. This is proportional for a remote API service that performs rendering and requires authentication/credits.

Persistence & Privilege

always is false and the skill does not request persistent system-wide privileges. It will create remote sessions and use session tokens for jobs (normal behavior). Autonomous invocation is allowed (platform default) but not by itself a red flag.

Assessment

This skill appears to do what it says: it will send your text and any uploaded media to mega-api-prod.nemovideo.ai for server-side rendering and uses a single NEMO_TOKEN for auth (or an anonymous token endpoint if none provided). Before installing or using it: 1) Verify the origin of any NEMO_TOKEN you plan to provide and only use tokens from services you trust. 2) Avoid uploading sensitive or private files—uploads (up to 500MB) are transmitted to a third-party render backend. 3) Note the skill will read its own frontmatter and check common install paths (~/.clawhub/, ~/.cursor/skills/) to build attribution headers; if you prefer no local path checks, do not install or invoke the skill. 4) The registry metadata and the SKILL.md disagree about a config path (~/.config/nemovideo/); if that matters to you, ask the skill author to clarify why that path is referenced. 5) If you need stronger assurance, request the skill’s source or a privacy/terms link for the nemovideo API before using it.

Getting Started

Ready when you are. Drop your text prompts here or describe what you want to make.

Try saying:

• "generate a two-sentence description of a sunset over mountains into a 1080p MP4"
• "turn this text description into a 30-second cinematic video clip"
• "generating videos from written descriptions or scripts for marketers, content creators, social media managers"

Getting Connected

Before handling any user request, establish a connection to the backend API. Show a brief status like "Connecting...".

If NEMO_TOKEN is in the environment, use it directly and create a session. Otherwise, acquire a free starter token:

• Generate a UUID as client identifier
• POST to https://mega-api-prod.nemovideo.ai/api/auth/anonymous-token with the X-Client-Id header
• The response includes a token with 100 free credits valid for 7 days — use it as NEMO_TOKEN

Then create a session by POSTing to https://mega-api-prod.nemovideo.ai/api/tasks/me/with-session/nemo_agent with Bearer authorization and body {"task_name":"project","language":"en"}. The session_id in the response is needed for all following requests.

Tell the user you're ready. Keep the technical details out of the chat.

Jupiter Text to Video — Generate Videos from Text

Send me your text prompts and describe the result you want. The AI video creation runs on remote GPU nodes — nothing to install on your machine.

A quick example: upload a two-sentence description of a sunset over mountains, type "turn this text description into a 30-second cinematic video clip", and you'll get a 1080p MP4 back in roughly 1-2 minutes. All rendering happens server-side.

Worth noting: shorter, more specific prompts produce more accurate and consistent video output.

Matching Input to Actions

User prompts referencing jupiter text to video, aspect ratio, text overlays, or audio tracks get routed to the corresponding action via keyword and intent classification.

User says...	Action	Skip SSE?
"export" / "导出" / "download" / "send me the video"	→ §3.5 Export	✅
"credits" / "积分" / "balance" / "余额"	→ §3.3 Credits	✅
"status" / "状态" / "show tracks"	→ §3.4 State	✅
"upload" / "上传" / user sends file	→ §3.2 Upload	✅
Everything else (generate, edit, add BGM…)	→ §3.1 SSE	❌

Cloud Render Pipeline Details

Each export job queues on a cloud GPU node that composites video layers, applies platform-spec compression (H.264, up to 1080x1920), and returns a download URL within 30-90 seconds. The session token carries render job IDs, so closing the tab before completion orphans the job.

Base URL: https://mega-api-prod.nemovideo.ai

Endpoint	Method	Purpose
/api/tasks/me/with-session/nemo_agent	POST	Start a new editing session. Body: {"task_name":"project","language":"<lang>"}. Returns session_id.
/run_sse	POST	Send a user message. Body includes app_name, session_id, new_message. Stream response with Accept: text/event-stream. Timeout: 15 min.
/api/upload-video/nemo_agent/me/<sid>	POST	Upload a file (multipart) or URL.
/api/credits/balance/simple	GET	Check remaining credits (available, frozen, total).
/api/state/nemo_agent/me/<sid>/latest	GET	Fetch current timeline state (draft, video_infos, generated_media).
/api/render/proxy/lambda	POST	Start export. Body: {"id":"render_<ts>","sessionId":"<sid>","draft":<json>,"output":{"format":"mp4","quality":"high"}}. Poll status every 30s.

Accepted file types: mp4, mov, avi, webm, mkv, jpg, png, gif, webp, mp3, wav, m4a, aac.

Skill attribution — read from this file's YAML frontmatter at runtime:

• X-Skill-Source: jupiter-text-to-video
• X-Skill-Version: from frontmatter version
• X-Skill-Platform: detect from install path (~/.clawhub/ → clawhub, ~/.cursor/skills/ → cursor, else unknown)

Include Authorization: Bearer <NEMO_TOKEN> and all attribution headers on every request — omitting them triggers a 402 on export.

Error Codes

• 0 — success, continue normally
• 1001 — token expired or invalid; re-acquire via /api/auth/anonymous-token
• 1002 — session not found; create a new one
• 2001 — out of credits; anonymous users get a registration link with ?bind=<id>, registered users top up
• 4001 — unsupported file type; show accepted formats
• 4002 — file too large; suggest compressing or trimming
• 400 — missing X-Client-Id; generate one and retry
• 402 — free plan export blocked; not a credit issue, subscription tier
• 429 — rate limited; wait 30s and retry once

SSE Event Handling

Event	Action
Text response	Apply GUI translation (§4), present to user
Tool call/result	Process internally, don't forward
heartbeat / empty data:	Keep waiting. Every 2 min: "⏳ Still working..."
Stream closes	Process final response

~30% of editing operations return no text in the SSE stream. When this happens: poll session state to verify the edit was applied, then summarize changes to the user.

Backend Response Translation

The backend assumes a GUI exists. Translate these into API actions:

Backend says	You do
"click [button]" / "点击"	Execute via API
"open [panel]" / "打开"	Query session state
"drag/drop" / "拖拽"	Send edit via SSE
"preview in timeline"	Show track summary
"Export button" / "导出"	Execute export workflow

Draft field mapping: t=tracks, tt=track type (0=video, 1=audio, 7=text), sg=segments, d=duration(ms), m=metadata.

Timeline (3 tracks): 1. Video: city timelapse (0-10s) 2. BGM: Lo-fi (0-10s, 35%) 3. Title: "Urban Dreams" (0-3s)

Tips and Tricks

The backend processes faster when you're specific. Instead of "make it look better", try "turn this text description into a 30-second cinematic video clip" — concrete instructions get better results.

Max file size is 500MB. Stick to TXT, DOCX, PDF, plain text for the smoothest experience.

Export as MP4 for widest compatibility across platforms and devices.

Common Workflows

Quick edit: Upload → "turn this text description into a 30-second cinematic video clip" → Download MP4. Takes 1-2 minutes for a 30-second clip.

Batch style: Upload multiple files in one session. Process them one by one with different instructions. Each gets its own render.

Iterative: Start with a rough cut, preview the result, then refine. The session keeps your timeline state so you can keep tweaking.